Bidirectional Topic Matching (BTM) is a novel method for cross-corpus topic modeling that quantifies thematic overlap and divergence between corpora. BTM is a flexible framework that can incorporate various topic modeling approaches, including BERTopic, Top2Vec, and Latent Dirichlet Allocation (LDA). It employs a dual-model approach, training separate topic models for each corpus and applying them reciprocally to enable comprehensive cross-corpus comparisons. This methodology facilitates the identification of shared themes and unique topics, providing nuanced insights into thematic relationships. A case study on climate news articles illustrates BTM’s utility by analyzing two distinct corpora: news coverage on climate change and articles focused on climate mitigation. The results reveal significant thematic overlaps and divergences, shedding light on how these two aspects of climate discourse are framed in the media.